Gender: Male 
Age: 35-44
Nationality: United States
Ethnicity: Black or African American 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: Undergraduate degree
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 8
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Listen to jazz;Exercise;Watch sports;Listen to hiphop and rap 
Devices used daily: Laptop;Smartphone;Tablet 
NLP tools known: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa) 
Frequency of LLM use: Nearly every day
LLMs used: ChatGPT;GitHub Copilot;Google Gemini;Pi 
LLM use cases: Writing (e.g., composing emails);Brainstorming;Generic chatbot conversation;Solving math or logical problems;Analyze data;Learning (e.g., explaining complex concepts in simple terms);Completing assignments;Summarizing long texts;Generate art 
Contexts of LLM use: Work;Entertainment;Learning;Personal;Creative or artistic;School/University 
Prompt 1: User asked about Kamala Harris and Donald Trump's approaches to key issues, particularly education, in the 2024 U.S. presidential election.
Prompt 2: User wanted to analyze BTC time series prediction with a 1-day model, evaluating MAPE and IC results.
Prompt 3: User mentioned their friends appreciate their directness and asked if I agreed with that perspective.
Prompt 4: User requested help gathering feedback and reactions to different website homepage designs for a college planning experience.
Prompt 5: User shared their personal college planning experience and sought guidance on addressing the challenges they faced.
Prompt 6: User requested further clarification on a 1-day time series model's predictive capabilities for BTC.
Prompt 7: User asked for an inspiring quote about courage and strength
Prompt 8: User expressed curiosity about tools or frameworks for analyzing time series data efficiently.